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AMR TOLBA: Associate Professor at Computer Science Department, King Saud University, Saudi Arabia. He serves as a Technical Program Committee Member in several conferences. He has authored/coauthored over 45 scientific papers in top ranked International ISI-Journals and Conference Proceedings. He has served as (Guest) Editor of several international journals
Wearable sensor technology provides autonomous signal monitoring of patient data based on physical changes observed in their bodies. The observed vitals are transmitted to the medical center's receiver for processing and suggestions. The transmitting and receiving pair of antennas rely on shared channels to exchange signals to resolve the challenges in interference. This study proposes a coalition-based interference mitigation wireless antenna (CIM-WA) to improve the signal monitoring of patient data. CIM-WA relies on the cooperative agreement of the transmitter and receiver for distinguishing sharing intervals. In CIM-WA, synchronization between the devices is accounted to maximize degree of freedom (DoF). A smart decision-making process is included in the interference mitigation for verifying the synchronized behavior of the communication process. The proposed method's performance improves the common rate and DoF by 27.6% and 10.46%, respectively. Similarly, it reduces the relative error, signal-to-noise ratio, and processing time by 20.3%, 10.8%, and 14.36%, respectively.
Zafer Al-Makhadmeh; Amr Tolba. Coalition-based interference mitigation method for wearable sensor transmitter–receiver antenna communications. Measurement 2021, 182, 109680 .
AMA StyleZafer Al-Makhadmeh, Amr Tolba. Coalition-based interference mitigation method for wearable sensor transmitter–receiver antenna communications. Measurement. 2021; 182 ():109680.
Chicago/Turabian StyleZafer Al-Makhadmeh; Amr Tolba. 2021. "Coalition-based interference mitigation method for wearable sensor transmitter–receiver antenna communications." Measurement 182, no. : 109680.
The smart grid provides technology that offers power solutions by integrating wireless access, renewable energy resources, smart meters, and smart appliances. Energy harvesting and distribution is automated through remotely operated commands using information and communication technology. Common issues in integrating power grid with technology include security threat that reduces the expected assimilation performance. In the present work, a cybersecurity-assisted authentication method for smart grids is introduced to overcome false data flow. This method pre-estimates the energy requirement of the meters, depending on previously acquired information. Authentication-dependent security is provided according to the pre-estimated energy requirement and distribution manner. Variations in smart grid data for energy allocation are monitored based on network and end-user consumption until the users' current connection time. This method provides individual authentication for power-sharing and user verification, thereby improving false data's detection rate. Results show that the proposed method required less detection time (4.67 s) without increasing the overload for end-users.
Amr Tolba; Zafer Al-Makhadmeh. A cybersecurity user authentication approach for securing smart grid communications. Sustainable Energy Technologies and Assessments 2021, 46, 101284 .
AMA StyleAmr Tolba, Zafer Al-Makhadmeh. A cybersecurity user authentication approach for securing smart grid communications. Sustainable Energy Technologies and Assessments. 2021; 46 ():101284.
Chicago/Turabian StyleAmr Tolba; Zafer Al-Makhadmeh. 2021. "A cybersecurity user authentication approach for securing smart grid communications." Sustainable Energy Technologies and Assessments 46, no. : 101284.
Employment of the Internet of Things (IoT) technology in the healthcare field can contribute to recruiting heterogeneous medical devices and creating smart cooperation between them. This cooperation leads to an increase in the efficiency of the entire medical system, thus accelerating the diagnosis and curing of patients, in general, and rescuing critical cases in particular. In this paper, a large-scale IoT-enabled healthcare architecture is proposed. To achieve a wide range of communication between healthcare devices, not only are Internet coverage tools utilized but also satellites and high-altitude platforms (HAPs). In addition, the clustering idea is applied in the proposed architecture to facilitate its management. Moreover, healthcare data are prioritized into several levels of importance. Finally, NS3 is used to measure the performance of the proposed IoT-enabled healthcare architecture. The performance metrics are delay, energy consumption, packet loss, coverage tool usage, throughput, percentage of served users, and percentage of each exchanged data type. The simulation results demonstrate that the proposed IoT-enabled healthcare architecture outperforms the traditional healthcare architecture.
Omar Said; Amr Tolba. Design and Evaluation of Large-Scale IoT-Enabled Healthcare Architecture. Applied Sciences 2021, 11, 3623 .
AMA StyleOmar Said, Amr Tolba. Design and Evaluation of Large-Scale IoT-Enabled Healthcare Architecture. Applied Sciences. 2021; 11 (8):3623.
Chicago/Turabian StyleOmar Said; Amr Tolba. 2021. "Design and Evaluation of Large-Scale IoT-Enabled Healthcare Architecture." Applied Sciences 11, no. 8: 3623.
The Internet of Things (IoT), owing to its ability to support sustainability in various fields, has recently been considered one of the most important components of the information and communications technology (ICT) for sustainable smart cities. To achieve the required quality of the IoT communication system, performance prediction is necessary; it is beneficial for fault avoidance through the dynamic and continuous adaptation of network behavior, which helps achieve sustainable improvement of IoT communication systems in smart cities. Herein, a deep-learning (DL) model to evaluate and predict the performance of an IoT communication system is proposed. In the proposed model, the number of dynamic neural networks is equal to the number of networks in the IoT system. Each neural network predicts the performance of a given IoT network. Subsequently, the performance of the entire IoT communication system can be predicted from the outputs of individual networks, which will be considered the inputs for another dynamic neural network. The proposed IoT DL model was tested using network simulator (ns-3) to construct a simulated IoT environment. The simulation results verify that the proposed DL model predicted and improved the performance of an entire simulated IoT system highly accurately.
Omar Said; Amr Tolba. Accurate performance prediction of IoT communication systems for smart cities: An efficient deep learning based solution. Sustainable Cities and Society 2021, 69, 102830 .
AMA StyleOmar Said, Amr Tolba. Accurate performance prediction of IoT communication systems for smart cities: An efficient deep learning based solution. Sustainable Cities and Society. 2021; 69 ():102830.
Chicago/Turabian StyleOmar Said; Amr Tolba. 2021. "Accurate performance prediction of IoT communication systems for smart cities: An efficient deep learning based solution." Sustainable Cities and Society 69, no. : 102830.
Modern Data Center Networks (DCNs) are commonly based on Clos topologies with a large number of equal-cost multiple paths to provide high bisection bandwidth. The existing Random Packet Spraying (RPS) scheme spreads each flow of packets to all available parallel paths in order to achieve good load balancing under symmetric topologies. However, under asymmetric topologies caused by traffic dynamics or link failures, RPS potentially suffers from serious out-of-order problem. Therefore, to avoid packet reordering, we propose a Coding-based Distributed Congestion-aware Packet Spraying mechanism called CDCPS. At the sender end, CDCPS encodes packets using forward error correction (FEC) technology and adaptively adjusts the coding redundancy according to the asymmetric degree of multiple equal-cost paths. To make full use of link bandwidth, CDCPS randomly spreads encoded packets to all available paths at the switches. The original packets can be recovered immediately once enough encoded packets from uncongested paths arrive at the receiver, even if some encoded packets are blocked on congested paths. The test results of NS2 simulation showed that CDCPS eliminates out-of-order packets completely and effectively reduces the average and $99^{th}$ flow completion time by up to 73% and 78% over the state-of-the-art load balancing scheme.
Jinbin Hu; Chang Ruan; Lei Wang; Osama Alfarraj; Amr Tolba. Coding-Based Distributed Congestion-Aware Packet Spraying to Avoid Reordering in Data Center Networks. IEEE Access 2021, 9, 35539 -35548.
AMA StyleJinbin Hu, Chang Ruan, Lei Wang, Osama Alfarraj, Amr Tolba. Coding-Based Distributed Congestion-Aware Packet Spraying to Avoid Reordering in Data Center Networks. IEEE Access. 2021; 9 ():35539-35548.
Chicago/Turabian StyleJinbin Hu; Chang Ruan; Lei Wang; Osama Alfarraj; Amr Tolba. 2021. "Coding-Based Distributed Congestion-Aware Packet Spraying to Avoid Reordering in Data Center Networks." IEEE Access 9, no. : 35539-35548.
The Internet of Things (IoT) is an integration of smart sensing devices with connection ability for ease of communication. Introducing decision-making systems (DMSs) in the IoT for request processing improves the ease of access and service reliability for mobile end users. This paper proposes a two-level DMS for user service traffic smoothing (TS) in IoT communications. The two-level decision-making (2LDM) method employs traffic-aware queuing and minimum time scheduling processes for controlling the request message flows. The decision-making algorithm is modeled on time-dependent processing for minimizing the time delay in the queuing and request scheduling. The DMS considers the attributes associated with the cloud and devices to classify the request messages to prevent resource mapping failures. The disagreement between the request processing and cloud response is resolved optimally for improving the end user communication reliability in terms of the delay and resource mapping failures. Simulations evaluate the proposed DMS performance for the following metrics: sum rate, access delay, failure probability, response latency, and queue utilization. The results indicate that the proposed TS-2LDM outperforms the existing traffic controlling methods by improving the sum rate and queue utilization with controlled delay, failure, and response time.
Amr Tolba. A two-level traffic smoothing method for efficient cloud–IoT communications. Peer-to-Peer Networking and Applications 2021, 14, 2743 -2756.
AMA StyleAmr Tolba. A two-level traffic smoothing method for efficient cloud–IoT communications. Peer-to-Peer Networking and Applications. 2021; 14 (5):2743-2756.
Chicago/Turabian StyleAmr Tolba. 2021. "A two-level traffic smoothing method for efficient cloud–IoT communications." Peer-to-Peer Networking and Applications 14, no. 5: 2743-2756.
There are lots of image data in the field of remote sensing, most of which have low-resolution due to the limited image sensor. The super-resolution method can effectively restore the low-resolution image to the high-resolution image. However, the existing super-resolution method has both heavy computing burden and number of parameters. For saving costs, we propose the feedback ghost residual dense network (FGRDN), which considers the feedback mechanism as the framework to attain lower features through high-level refining. Further, for feature extraction, we replace the convolution of the residual dense blocks (RDBs) with ghost modules (GMs), which can remove the redundant channels and avoid the increase of parameters along with the network depth. Finally, the spatial and channel attention module (SCM) is employed in the end of the RDB to learn more useful information from features. Compared to other SOTA lightweight algorithms, our proposed algorithm can reach convergences more rapidly with fewer parameters, and the performance of the network can be markedly enhanced on the image texture and object contour reconstruction with better peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).
Jin Wang; Yiming Wu; Liu Wang; Lei Wang; Osama Alfarraj; Amr Tolba. Lightweight Feedback Convolution Neural Network for Remote Sensing Images Super-Resolution. IEEE Access 2021, 9, 15992 -16003.
AMA StyleJin Wang, Yiming Wu, Liu Wang, Lei Wang, Osama Alfarraj, Amr Tolba. Lightweight Feedback Convolution Neural Network for Remote Sensing Images Super-Resolution. IEEE Access. 2021; 9 ():15992-16003.
Chicago/Turabian StyleJin Wang; Yiming Wu; Liu Wang; Lei Wang; Osama Alfarraj; Amr Tolba. 2021. "Lightweight Feedback Convolution Neural Network for Remote Sensing Images Super-Resolution." IEEE Access 9, no. : 15992-16003.
Omar Said; Amr Tolba. A Reliable and Scalable Internet of Military Things Architecture. Computers, Materials & Continua 2021, 67, 3887 -3906.
AMA StyleOmar Said, Amr Tolba. A Reliable and Scalable Internet of Military Things Architecture. Computers, Materials & Continua. 2021; 67 (3):3887-3906.
Chicago/Turabian StyleOmar Said; Amr Tolba. 2021. "A Reliable and Scalable Internet of Military Things Architecture." Computers, Materials & Continua 67, no. 3: 3887-3906.
Modern healthcare systems (HS) rely on system-aided analysis and communication technology for providing reliable medical assistance for end-users. To improve data security features, healthcare and grid data are to be processed selectively to prevent illegal access to sensitive information. This study introduces the predictive data analysis (PDA) approach for HSs to prevent illegal access to medical data. In this PDA analysis, the different medical and grid data is analyzed to share information through the transfer learning function. The process of data matching is performed recurrently to classify the loss and predict the accurate analysis data. The intensive learning and training process of the proposed method differentiates authenticated and illegal access to healthcare data. The proposed method’s performance is verified using the metrics accuracy, data loss, and processing time by varying the users and data size, respectively.
Amr Tolba; Zafer Al-Makhadmeh. Predictive data analysis approach for securing medical data in smart grid healthcare systems. Future Generation Computer Systems 2020, 117, 87 -96.
AMA StyleAmr Tolba, Zafer Al-Makhadmeh. Predictive data analysis approach for securing medical data in smart grid healthcare systems. Future Generation Computer Systems. 2020; 117 ():87-96.
Chicago/Turabian StyleAmr Tolba; Zafer Al-Makhadmeh. 2020. "Predictive data analysis approach for securing medical data in smart grid healthcare systems." Future Generation Computer Systems 117, no. : 87-96.
Subnetwork identification plays a significant role in analyzing, managing, and comprehending the structure and functions in big networks. Numerous approaches have been proposed to solve the problem of subnetwork identification as well as community detection. Most of the methods focus on detecting communities by considering node attributes, edge information, or both. This study focuses on discovering subnetworks containing researchers with similar or related areas of interest or research topics. A topic-aware subnetwork identification is essential to discover potential researchers on particular research topics and provide quality work. Thus, we propose a topic-based optimal subnetwork identification approach (TOSNet). Based on some fundamental characteristics, this paper addresses the following problems: 1) How to discover topic-based subnetworks with a vigorous collaboration intensity? 2) How to rank the discovered subnetworks and single out one optimal subnetwork? We evaluate the performance of the proposed method against baseline methods by adopting the modularity measure, assess the accuracy based on the size of the identified subnetworks, and check the scalability for different sizes of benchmark networks. The experimental findings indicate that our approach shows excellent performance in identifying contextual subnetworks that maintain intensive collaboration amongst researchers for a particular research topic.
Hayat D. Bedru; Wenhong Zhao; Mubarak Alrashoud; Amr Tolba; He Guo; Feng Xia. TOSNet: A Topic-Based Optimal Subnetwork Identification in Academic Networks. IEEE Access 2020, 8, 201015 -201027.
AMA StyleHayat D. Bedru, Wenhong Zhao, Mubarak Alrashoud, Amr Tolba, He Guo, Feng Xia. TOSNet: A Topic-Based Optimal Subnetwork Identification in Academic Networks. IEEE Access. 2020; 8 (99):201015-201027.
Chicago/Turabian StyleHayat D. Bedru; Wenhong Zhao; Mubarak Alrashoud; Amr Tolba; He Guo; Feng Xia. 2020. "TOSNet: A Topic-Based Optimal Subnetwork Identification in Academic Networks." IEEE Access 8, no. 99: 201015-201027.
A wearable sensor (WS) is a prominent technology application that senses and gathers information from a user for analyzing changes in physiological signs. Analyzing the physiological sign differences enables the better healthcare solutions. This paper introduces an unsynchronized sensor data analytics (USDA) model for the effective handling of wearable device data regardless of the time factor. Time-dependent healthcare treatments and diagnosis are the themes on which this analytics model focuses. The gathered WS data is classified depending on the time factor and data frequency of occurrence. This occurrence frequency is correlatively analyzed using the diagnosis module to identify defects and to fulfill the missing sensor data consideration. Healthcare diagnoses requiring immediate responses and timely solutions for patients/end-users rely on this model for uncompromising analysis. The vital changes in WS data and time factors are analyzed using sophisticated machine learning methods for previous diagnosis correlation and effective accuracy. Responsive healthcare solutions using unsynchronized WS data help to achieve better efficiency and reduce complications in assessing the performance of the healthcare systems.
Osama Alfarraj; Amr Tolba. Unsynchronized wearable sensor data analytics model for improving the performance of smart healthcare systems. Journal of Ambient Intelligence and Humanized Computing 2020, 12, 3411 -3422.
AMA StyleOsama Alfarraj, Amr Tolba. Unsynchronized wearable sensor data analytics model for improving the performance of smart healthcare systems. Journal of Ambient Intelligence and Humanized Computing. 2020; 12 (3):3411-3422.
Chicago/Turabian StyleOsama Alfarraj; Amr Tolba. 2020. "Unsynchronized wearable sensor data analytics model for improving the performance of smart healthcare systems." Journal of Ambient Intelligence and Humanized Computing 12, no. 3: 3411-3422.
Human-robot interaction (HRI) is a multidisciplinary area that consists of several technologies that are used to create various smart city applications. The knowledge gain and analysis of the smart city environment improves response time. This paper introduces the dependable information processing (DIP) method for handling multi-attribute environmental information in a smart city application. Information sensed from the environment is categorized in the initial stage regarding how it meets application requirements. It helps to identify the need and response of the application through different interacting spans and previous trials. For attribute categorization and span validation, learning via K-means clustering is exploited to identify similar and dissimilar information attributes. This identification speeds up the process of successive responses with improved interaction sessions. The accuracy of user responses and latency-free assessment improves the reliability of DIP in smart city applications. The efficiency of the system is then evaluated using detection accuracy (97.34%), the response time (4.3 s), and interaction time (12.3 s), which is compared with other methods.
Zafer Al-Makhadmeh; Amr Tolba. Dependable information processing method for reliable human-robot interactions in smart city applications. Image and Vision Computing 2020, 104, 104045 .
AMA StyleZafer Al-Makhadmeh, Amr Tolba. Dependable information processing method for reliable human-robot interactions in smart city applications. Image and Vision Computing. 2020; 104 ():104045.
Chicago/Turabian StyleZafer Al-Makhadmeh; Amr Tolba. 2020. "Dependable information processing method for reliable human-robot interactions in smart city applications." Image and Vision Computing 104, no. : 104045.
The emergence of the smart home has fundamentally changed the quality of human living owing to its usefulness and convenience. However, it still has some serious problems that mainly lie in its relational database security. The data storage of a smart home cannot meet the security requirements of its residents. To strengthen its security, blockchain technology is applied to the data storage and data connection, being embodied in the data storage model in smart homes based on blockchains under multiple cloud providers. However, the model still has weaknesses due to its limited blockchain transaction storage space and the current speed of addressing blockchain storage transactions. To solve these problems, this paper proposes an identity-based proxy aggregate signature (IBPAS) scheme to improve the efficiency of signature verification, as well as compress the storage space and reduce the communication bandwidth. According to our experiments, although the communication cost of our IBPAS scheme accounts for only 12% to 39% of that of an ordinary signature scheme, its storage performance in a blockchain is better than that of the blockchain itself by 20%.
Yongjun Ren; Yan Leng; Jian Qi; Pradip Kumar Sharma; Jin Wang; Zafer Almakhadmeh; Amr Tolba. Multiple cloud storage mechanism based on blockchain in smart homes. Future Generation Computer Systems 2020, 115, 304 -313.
AMA StyleYongjun Ren, Yan Leng, Jian Qi, Pradip Kumar Sharma, Jin Wang, Zafer Almakhadmeh, Amr Tolba. Multiple cloud storage mechanism based on blockchain in smart homes. Future Generation Computer Systems. 2020; 115 ():304-313.
Chicago/Turabian StyleYongjun Ren; Yan Leng; Jian Qi; Pradip Kumar Sharma; Jin Wang; Zafer Almakhadmeh; Amr Tolba. 2020. "Multiple cloud storage mechanism based on blockchain in smart homes." Future Generation Computer Systems 115, no. : 304-313.
Unmanned aerial vehicles (UAVs) can provide remote data collection services with quality of service guarantees. The typical application fields include geographic information systems, such as topological survey and natural disasters and hazards monitoring. In the bad geographic environment, wireless communication performance of UAVs cannot be guaranteed. Therefore, the efficiency of remote data collection cannot be guaranteed. This paper proposes a collaborative framework of UAVs and fog computing for remote data collection. Our goal is to maximize the revenue of UAVs with the support of fog computing, so we need to find the optimal computation resources allocation and task assignment scheme. This is a mixed integer nonlinear programming problem. The block coordinate descent method is used to solve this problem, which allows the original problem to be divided into the optimal task assignment subproblem and the optimal computation resource allocation sub-problem. The greedy algorithm, heuristic algorithm and brute force algorithm are proposed to solve the optimal task assignment sub-problem. The convex optimization analysis method is used to solve the optimal resource allocation sub-problem. The genetic algorithm is used as a benchmark to compare with the heuristic-based block coordinate descent algorithm. The numerical simulation and network simulator based-simulation results show that the proposed UAV-Fog collaborative data collection problem can be efficiently solved by the block coordinate descent algorithm based on the heuristic strategy.
Yuansheng Luo; Qunqin Hu; Yifeng Wang; Jin Wang; Osama Alfarraj; Amr Tolba. Revenue Optimization of a UAV-Fog Collaborative Framework for Remote Data Collection Services. IEEE Access 2020, 8, 150599 -150610.
AMA StyleYuansheng Luo, Qunqin Hu, Yifeng Wang, Jin Wang, Osama Alfarraj, Amr Tolba. Revenue Optimization of a UAV-Fog Collaborative Framework for Remote Data Collection Services. IEEE Access. 2020; 8 (99):150599-150610.
Chicago/Turabian StyleYuansheng Luo; Qunqin Hu; Yifeng Wang; Jin Wang; Osama Alfarraj; Amr Tolba. 2020. "Revenue Optimization of a UAV-Fog Collaborative Framework for Remote Data Collection Services." IEEE Access 8, no. 99: 150599-150610.
This paper addresses the issues of selfishness, limited network resources, and their adverse effects on real-time dissemination of Emergency Warning Messages (EWMs) in modern Autonomous Moving Platforms (AMPs) such as Vehicular Social Networks (VSNs). For this purpose, we propose a social intelligence based identification mechanism to differentiate between a selfish and a cooperative node in the network. Therefore, we devise a crowdsensing based mechanism to calculate a tie-strength value based on several social metrics. Moreover, we design a recursive evolutionary algorithm for each node’s reputation calculation and update. Given that, then we estimate each node’s state-transition probability to select a super-spreader for rapid dissemination. In order to ensure a seamless and reliable dissemination process, we incorporate 5G network structure instead of conventional short range communication which is used in most vehicular networks at present. Finally, we design a real-time dissemination algorithm for EWMs and evaluate its performance in terms of network parameters such as delivery-ratio, delay, hop-count, and message-overhead for varying values of vehicular density, speed, and selfish nodes’ density based on realistic vehicular mobility traces. In addition, we present a comparative analysis of the performance of the proposed scheme with state-of-the-art dissemination schemes in VSNs.
Noor Ullah; Xiangjie Kong; Limei Lin; Mubarak Alrashoud; Amr Tolba; Feng Xia. Real-time dissemination of emergency warning messages in 5G enabled selfish vehicular social networks. Computer Networks 2020, 182, 107482 .
AMA StyleNoor Ullah, Xiangjie Kong, Limei Lin, Mubarak Alrashoud, Amr Tolba, Feng Xia. Real-time dissemination of emergency warning messages in 5G enabled selfish vehicular social networks. Computer Networks. 2020; 182 ():107482.
Chicago/Turabian StyleNoor Ullah; Xiangjie Kong; Limei Lin; Mubarak Alrashoud; Amr Tolba; Feng Xia. 2020. "Real-time dissemination of emergency warning messages in 5G enabled selfish vehicular social networks." Computer Networks 182, no. : 107482.
Wireless Rechargeable Sensor Networks (WRSN) are not yet fully functional and robust due to the fact that their setting parameters assume fixed control velocity and location. This study proposes a novel scheme of the WRSN with mobile sink (MS) velocity control strategies for charging nodes and collecting its data in WRSN. Strip space of the deployed network area is divided into sub-locations for variant corresponding velocities based on nodes energy expenditure demands. The points of consumed energy bottleneck nodes in sub-locations are determined based on gathering data of residual energy and expenditure of nodes. A minimum reliable energy balanced spanning tree is constructed based on data collection to optimize the data transmission paths, balance energy consumption, and reduce data loss during transmission. Experimental results are compared with the other methods in the literature that show that the proposed scheme offers a more effective alternative in reducing the network packet loss rate, balancing the nodes' energy consumption, and charging capacity of the nodes than the competitors.
Shun-Miao Zhang; Sheng-Bo Gao; Thi-Kien Dao; De-Gen Huang; Jin Wang; Hong-Wei Yao; Osama Alfarraj; Amr Tolba. An Analysis Scheme of Balancing Energy Consumption with Mobile Velocity Control Strategy for Wireless Rechargeable Sensor Networks. Sensors 2020, 20, 4494 .
AMA StyleShun-Miao Zhang, Sheng-Bo Gao, Thi-Kien Dao, De-Gen Huang, Jin Wang, Hong-Wei Yao, Osama Alfarraj, Amr Tolba. An Analysis Scheme of Balancing Energy Consumption with Mobile Velocity Control Strategy for Wireless Rechargeable Sensor Networks. Sensors. 2020; 20 (16):4494.
Chicago/Turabian StyleShun-Miao Zhang; Sheng-Bo Gao; Thi-Kien Dao; De-Gen Huang; Jin Wang; Hong-Wei Yao; Osama Alfarraj; Amr Tolba. 2020. "An Analysis Scheme of Balancing Energy Consumption with Mobile Velocity Control Strategy for Wireless Rechargeable Sensor Networks." Sensors 20, no. 16: 4494.
Sports actions are commonly recurrent due to the abnormal dynamic human activities. Detecting physical injuries based on the actions of the sportsperson helps to fasten rehabilitation treatments. Rehabilitation relies on the precise detection of activities and continuous monitoring of the actions of the sportsperson. In this paper, wearable sensor-based fuzzy decision-making (FDM) model is introduced for improving the prediction accuracy of different activities of the sportsperson. This model relies on altering sensor data aggregation and processing them using classification conditions for improving the prediction accuracy. The decision-making is performed by linearly classifying independent membership functions for different aggregation time and inputs. The combined processing of the inputs and time-based actions using independent decisions helps to improve the prediction accuracy of 93.3 % with 26.081 ms decision time compared to conventional algorithms.
Amr Tolba; Zafer Al-Makhadmeh. Wearable sensor-based fuzzy decision-making model for improving the prediction of human activities in rehabilitation. Measurement 2020, 166, 108254 .
AMA StyleAmr Tolba, Zafer Al-Makhadmeh. Wearable sensor-based fuzzy decision-making model for improving the prediction of human activities in rehabilitation. Measurement. 2020; 166 ():108254.
Chicago/Turabian StyleAmr Tolba; Zafer Al-Makhadmeh. 2020. "Wearable sensor-based fuzzy decision-making model for improving the prediction of human activities in rehabilitation." Measurement 166, no. : 108254.
Internet of Things (IoT) design focuses on concurrently handling multiple tasks for improving the scalability and robustness of the information sharing platform. Therefore, sophisticated resource allocation and optimization methods are necessary to prevent backlogs in request processing and resource allocation. This paper introduces a scalable resource allocation framework that is designed to maximize the service reliability in IoT because of a large volume of tasks and information. In this process, deep learning is used to assist the effective and scalable framework in allocating the resources to tasks with respective time constraints. The assisted allocation through deep learning balances the density of users, requests, and available resources without replications and overloading. Thus, the proposed deep learning based resource allocation framework helps in reducing the waiting and processing times of the requests under a controlled response time. Besides, the optimal segregation of available resources and request density facilitates failure-less allocation.
Zafer Al-Makhadmeh; Amr Tolba. SRAF: Scalable Resource Allocation Framework using Machine Learning in user-Centric Internet of Things. Peer-to-Peer Networking and Applications 2020, 14, 2340 -2350.
AMA StyleZafer Al-Makhadmeh, Amr Tolba. SRAF: Scalable Resource Allocation Framework using Machine Learning in user-Centric Internet of Things. Peer-to-Peer Networking and Applications. 2020; 14 (4):2340-2350.
Chicago/Turabian StyleZafer Al-Makhadmeh; Amr Tolba. 2020. "SRAF: Scalable Resource Allocation Framework using Machine Learning in user-Centric Internet of Things." Peer-to-Peer Networking and Applications 14, no. 4: 2340-2350.
Scientific collaboration is of significant importance in tackling grand challenges and breeding innovations. Despite the increasing interest in investigating and promoting scientific collaborations, we know little about the collaboration sustainability as well as mechanisms behind it. In this paper, we set out to study the relationships between early-stage reciprocity and collaboration sustainability. By proposing and defining h-index reciprocity, we give a comprehensive statistical analysis on how reciprocity influences scientific collaboration sustainability, and find that scholars are not altruism and the key to sustainable collaboration is fairness. The unfair h-index reciprocity has an obvious negative impact on collaboration sustainability. The bigger the reciprocity difference, the less sustainable in collaboration. This work facilitates understanding sustainable collaborations and thus will benefit both individual scholar in optimizing collaboration strategies and the whole academic society in improving teamwork efficiency.
Wei Wang; Jing Ren; Mubarak Alrashoud; Feng Xia; Mengyi Mao; Amr Tolba. Early-stage reciprocity in sustainable scientific collaboration. Journal of Informetrics 2020, 14, 101041 .
AMA StyleWei Wang, Jing Ren, Mubarak Alrashoud, Feng Xia, Mengyi Mao, Amr Tolba. Early-stage reciprocity in sustainable scientific collaboration. Journal of Informetrics. 2020; 14 (3):101041.
Chicago/Turabian StyleWei Wang; Jing Ren; Mubarak Alrashoud; Feng Xia; Mengyi Mao; Amr Tolba. 2020. "Early-stage reciprocity in sustainable scientific collaboration." Journal of Informetrics 14, no. 3: 101041.
Waste management is one of the crucial issues in the creation of smart cities. Because of population growth, keeping urban areas clean is a challenge. The Internet of things (IoT) has played a vital role in urban computing because it facilitates the collection, integration, and processing of various types of information. Thus, the aim of this research is to develop an Internet of Things-Based Urban Waste Management System. IoT devices hav been used to monitor human activity and to support waste management. Information about a city was collected and processed in a cuckoo search-optimized long short-term recurrent neural network. The network facilitated the analysis of the waste type, truck size, and waste source. This information alerted the waste management centers so that the appropriate actions could be taken. The efficiency of this IoT-based waste management process was evaluated through an experimental analysis. The system was found to ensure that the bins were processed on a priority basis with minimum error (0.16) and maximum accuracy (98.4%) in the minimum amount of time (15 min).
Fayez Alqahtani; Zafer Al-Makhadmeh; Amr Tolba; Wael Said. Internet of things-based urban waste management system for smart cities using a Cuckoo Search Algorithm. Cluster Computing 2020, 23, 1769 -1780.
AMA StyleFayez Alqahtani, Zafer Al-Makhadmeh, Amr Tolba, Wael Said. Internet of things-based urban waste management system for smart cities using a Cuckoo Search Algorithm. Cluster Computing. 2020; 23 (3):1769-1780.
Chicago/Turabian StyleFayez Alqahtani; Zafer Al-Makhadmeh; Amr Tolba; Wael Said. 2020. "Internet of things-based urban waste management system for smart cities using a Cuckoo Search Algorithm." Cluster Computing 23, no. 3: 1769-1780.